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An Exploratory Study: Forecasting Winning Bid Prices in Online Auction Markets
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Author(s): Hong-Il Kim (Hanyang University, Korea), Bongjun Kim (Electronics and Telecommunications Research Institute, Korea), Sungbin Cho (Konkuk University, Korea)and Seung Baek (Hanyang University, Korea)
Copyright: 2003
Pages: 3
Source title:
Information Technology & Organizations: Trends, Issues, Challenges & Solutions
Source Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-59140-066-0.ch228
ISBN13: 9781616921248
EISBN13: 9781466665330
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Abstract
To solve the information asymmetry problem in online auction markets, this study suggests and validates forecasting models of winning bid prices. Specially, it explores the usability of Neural network, Bayesian network, and Logistic regression in building the forecasting models. This research empirically shows that, in forecasting winning bid prices in online auction markets, data mining techniques such as Bayesian network and Neural network, have showed better performance that a traditional statistical model, Logistic regression. In addition, depending on the nature of data and the data transformation strategy, we have to select an appropriate data mining technique carefully.
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